Futurists like Ray Kurzweil have long predicted that one day computers would have computational ability that rivals that of the human brain. Until now, one of the biggest hurdles has been limiting the energy required to process trillions of calculations.

The human brain uses about 20 W of power to process all of the information that is being fed to it by our senses. In comparison a computer that could process information equal to the human brain would consume around 100 MW of power, roughly enough energy to power 1 million homes for an hour.

To move past this major stumbling block scientists at IBM’s Brain Lab have begun leveraging the innovations of three seemingly disparate fields, supercomputing, neuroscience and nanotechnology, into a new paradigm that they call cognitive computing.

The thrust of cognitive computing’s new direction lays in its redefinition of what a computer should do. IBM researcher Dr. John Arthur defines the problem this way:

“The purpose of these Neuro-morphic chips is to build systems that do things efficiently that computers do poorly. So current computers are great at adding numbers… but they do really poorly at recognizing people faces and recognizing objects… something that our brains do really, really well at. The hope is that we can build very large systems that can do recognition tasks in an automatic way.”

To reach a point where computers are capable of completing recognition tasks the Brain Lab is building their systems around two symbolic features, “Neurons” and “Synapses”.

In this system the Neurons are chips that send and receive signals from other neurons within their local sphere. Neurons are divided into two groups, excitatory and inhibitory. When sending a signal, excitatory neurons will activate other neurons in their local sphere. In contrast, when an inhibitory neuron fires off a signal its neighbors are inhibited from reacting.

Synapses, while not at the core of signal generation, are equally as important. Synapses serve as the pathways through which neural signals travel. As the system generates more and more signals, synaptic connections evolve to have greater value based on the amount of signals that pass over a signal synapse. Adding this weighted value trains the neurons to send signals over the most effective routes.

By defining the most effective means of transporting information IBM’s system can reduce processing time by focusing on only the neurons and synapses that are within the active communication network. This “event-driven” method of processing closely resembles the way that the human brain processes signals.

While IBM is making notable progress down the road to a functional artificial human brain they haven’t created one yet. Some optimistic futurists believe that projects like Blue Brain will “exceed human intellectual capacity by around 2015” but scaling down the power consumption of those projects will require the Brain Lab to make major breakthroughs in a remarkably short amount of time.